论文标题
可解释的人工智能的平均值帐户
A Means-End Account of Explainable Artificial Intelligence
论文作者
论文摘要
可解释的人工智能（XAI）旨在为那些被认为不透明的机器学习方法提供解释。但是，关于这意味着什么以及如何实现它存在很大的分歧。作者不同意应解释的内容（主题），应向谁解释（利益相关者），应如何解释某些东西（工具）以及为什么要解释某事（目标）。在本文中，我采用了从平均值认识论的见解来构建该领域。根据平均末端认识论，应在理性上采用不同的手段来实现不同的认识论目的。因此，适用于XAI，不同的主题，利益相关者和目标，因此需要不同的工具。我将其称为Xai的均值帐户。平均值帐户具有描述性和规范性的组成部分：一方面，我展示了特定的平均值关系是如何产生对XAI领域现有贡献的分类法的；另一方面，我认为可以通过分析给定主题，利益相关者和目标是否处方来评估XAI方法的适用性。
Explainable artificial intelligence (XAI) seeks to produce explanations for those machine learning methods which are deemed opaque. However, there is considerable disagreement about what this means and how to achieve it. Authors disagree on what should be explained (topic), to whom something should be explained (stakeholder), how something should be explained (instrument), and why something should be explained (goal). In this paper, I employ insights from means-end epistemology to structure the field. According to means-end epistemology, different means ought to be rationally adopted to achieve different epistemic ends. Applied to XAI, different topics, stakeholders, and goals thus require different instruments. I call this the means-end account of XAI. The means-end account has a descriptive and a normative component: on the one hand, I show how the specific means-end relations give rise to a taxonomy of existing contributions to the field of XAI; on the other hand, I argue that the suitability of XAI methods can be assessed by analyzing whether they are prescribed by a given topic, stakeholder, and goal.
